Leaf area index (LAI) is a key variable for the understanding of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the ...environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors.
This study proposed a method to estimate LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM). The CSDM described the temporal evolution of the LAI as function of the accumulated daily air temperature as measured from classical ground meteorological stations.
The retrieval performances were evaluated for two different data sets: first, a data set simulated by the RTM but taking into account realistic measurement conditions and uncertainties resulting from different error sources; second, an experimental data set acquired over maize crops the Blue Earth City area (USA) in 1998. Results showed that the proposed approach improved significantly the retrieval performances for LAI mainly by smoothing the residual errors associated to each individual observation. In addition it provides a way to describe in a continuous manner the LAI time course from a limited number of observations during the growth cycle.
Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they ...cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).
The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the ...state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals.
Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. ...Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed.
Four one-dimensional radiative transfer models are compared in direct and inverse modes. These models are combinations of the PROSPECT leaf optical properties model and the SAIL (Scattering by ...Arbitrarily Inclined Leaves), IAPI, KUUSK, and NADI (New Advanced Discrete Model) canopy reflectance models. To evaluate their ability to estimate canopy biophysical parameters, inversions were first performed on synthetic reflectance spectra (10 wavelengths in the visible and near-infrared). The simulated spectral and directional reflectances showed good agreement among the four models. A 1997 airborne experiment in the United States was used to test their performance on real data. This experiment gathered a unique data set composed primarily of 200 reflectance spectra acquired over corn (
Zea mays L.) and soybean (
Glycine max) fields, and the corresponding ground truth (chlorophyll a+b content and leaf area index). Only the first three models, which ran fast enough to allow the processing of a large data set, were actually inverted by iterative optimization techniques. Inversions were conducted in successive stages where the number of retrieved parameters was reduced. No significant difference can be observed between the three models. Globally, the leaf mesophyll structure parameter and leaf dry matter content couldn't be estimated. The chlorophyll content, the leaf area index, and the mean leaf inclination angle yielded better results, although the latter wasn't validated due to missing ground data. Assuming that model inversion by iterative optimization techniques is a promising method to extract information on plant canopies, the SAIL and KUUSK models, which perform well in terms of accuracy and running time, proved to be good candidates for remote sensing application in ecology or agriculture (precision farming).
Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations ...contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.
The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the ...state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals.
•The performance of 12 processors is investigated for Sentinel-2 and Landsat 8 data.•Aerosol Optical Depth retrievals' errors for Sentinel-2 and Landsat 8 were 0.1–0.2.•Water vapour retrievals' errors for Sentinel-2 were 0.2–0.4 g/cm2.•Low standard deviations (<0.04) between on-site and estimated surface reflectance.•Lower surface reflectance uncertainties for SWIR and NIR than visible bands.•Surface reflectance uncertainties < 0.01 for the best performing processors.
To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer ...(MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling . All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies - ISOP - system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination - R
2
= 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R
2
= 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R
2
= 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.
Service innovant unique au monde, Farmstar valorise les images satellites et des modeles agronomiques pour aider les agriculteurs dans leurs décisions d'apport d'azote en cours de campagne. Son ...succes, avec plus 16 000 agriculteurs abonnés exploitant plus de 700 000 hectares, s'explique par sa grande precision sur un élément minéral qui, d'une part, est, en France, avant l'eau, le principal facteur limitant la production agricole tant en quantité qu'en qualité, et qui, d'autre part, peut étre responsable d'une baisse de la qualité des eaux (augmentation de la teneur en nitrates des nappes phréatiques). Les bénéfices générés concernent la rentabilité économique (gains de rendement, réduction des doses d'intrants, récolte de grains de meilleure qualité) et les enjeux environnementaux et sociétaux (en évitant tout type d'exces d'azote, en traçant et en justifiant les interventions des agriculteurs). Par ailleurs, grâce aux cartes de zonage livrées aux agriculteurs, chacun d'eux peut faire varier les doses nécessaires en fonction des besoins des plantes en différents points d'une méme parcelle. Cette réussite enviée par beaucoup résulte de l'agrégation de compétences complémentaires entre les agronomes d'ARVALIS et les experts en télédétection d'Airbus.
PDF
